• Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA.
• Department of Applied Health Science, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA.
• Department of Nutritional Sciences, College of Agricultural and Life Sciences, University of Wisconsin, Madison, WI, USA.
• Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA, USA.

BACKGROUND: The Goldberg cutoffs are used to decrease bias in self-reported estimates of energy intake (EISR). Whether the cutoffs reduce and eliminate bias when used in regressions of health outcomes has not been assessed.

OBJECTIVE: We examined whether applying the Goldberg cutoffs to data used in nutrition studies could reliably reduce or eliminate bias.

METHODS: We used data from the Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy (CALERIE), the Interactive Diet and Activity Tracking in American Association of Retired Persons (IDATA) study, and the National Diet and Nutrition Survey (NDNS). Each data set included EISR, energy intake estimated from doubly labeled water (EIDLW) as a reference method, and health outcomes including baseline anthropometric, biomarker, and behavioral measures and fitness test results. We conducted 3 linear regression analyses using EISR, a plausible EISR based on the Goldberg cutoffs (EIG), and EIDLW as an explanatory variable for each analysis. Regression coefficients were denoted ${\hat{\beta }_{\rm SR}}$, ${\hat{\beta }_{\rm G}}$, and ${\hat{\beta }_{\rm DLW}}$, respectively. Using the jackknife method, bias from ${\hat{\beta }_{\rm SR}}$ compared with ${\hat{\beta }_{\rm DLW}}$ and remaining bias from ${\hat{\beta }_{\rm G}}$ compared with ${\hat{\beta }_{\rm DLW}}$ were estimated. Analyses were repeated using Pearson correlation coefficients.

RESULTS: The analyses from CALERIE, IDATA, and NDNS included 218, 349, and 317 individuals, respectively. Using EIG significantly decreased the bias only for a subset of those variables with significant bias: weight (56.1%; 95% CI: 28.5%, 83.7%) and waist circumference (WC) (59.8%; 95% CI: 33.2%, 86.5%) with CALERIE, weight (20.8%; 95% CI: -6.4%, 48.1%) and WC (17.3%; 95% CI: -20.8%, 55.4%) with IDATA, and WC (-9.5%; 95% CI: -72.2%, 53.1%) with NDNS. Furthermore, bias significantly remained even after excluding implausible data for various outcomes. Results obtained with Pearson correlation coefficient analyses were qualitatively consistent.

CONCLUSIONS: Some associations between EIG and outcomes remained biased compared with associations between EIDLW and outcomes. Use of the Goldberg cutoffs was not a reliable method for eliminating bias.